It has long been recognized that the information processing capabilities of traditional computing systems differ significantly from those of animal intelligence, including human intelligence. Living systems of animal intelligence excel at solving problems by appealing to analogies with past experience. Traditional digital computing systems excel at following a sequence of steps to arrive at a result. Traditional analog computing systems excel at very specific computational tasks, namely those that involve solving differential equations. Traditional connectionist computing systems excel at implementing interpolations from one multidimensional space to another, based on examples connecting the two spaces at points. The traditional computational systems generically grouped under the term "artificial intelligence systems" do in fact attempt to emulate processing capabilities found in animal intelligence, but the success of such attempts has been limited.
The information processing capabilities of living systems of animal intelligence are analogic and very robust in nature. Such systems excel at seeing and doing things that are analogous to things seen and done in the past and are very forgiving of errors in data. The term, correlithm, is often used to describe generically the processes for which living information processing systems excel, to distinguish them from algorithms, the processes for which traditional information processing systems excel. The construction of computing systems able to approach or duplicate these and other unique information processing capabilities of living systems has been one of the most elusive and intensely pursued goals of research and development in the entire history of constructed computing systems, yet results to date have been meager.
Traditional computing systems simply are not very good at the sorts of tasks at which living computing systems excel.
Traditional digital computing systems are exceedingly precise, but brittle. Computation in these systems is accomplished through an exquisitely orchestrated sequence of discrete steps involving exact data. Errors of so little as a single bit, the smallest unit of data distinction available in these systems, in either the sequence of steps or in the data, will frequently render the result of the computation useless. Elaborate efforts have been directed over the years at both the hardware systems and at the software programs such hardware executes, to avoid or minimize such errors. The precision of such traditional digital computing systems and the success in avoiding the brittleness of execution imposed by such precision have both been key strengths in the growth of the digital computing industry. Precision and brittleness, however, are very far from the robust, analogic information processing found in living systems.
Typically, traditional analog computing systems are used to solve mathematical equations, particularly differential equations, through exploration. Electronic circuit equivalents of mathematical operators such as addition, multiplication, integration, and so forth are interconnected to produce an overall electronic equivalent of a specific equation. By varying parameters of the individual circuits, the overall electronic equivalent is exercised to explore the characteristics of the equation it implements. While analog computing systems provide a highly useful functionality, it is clear that the types of computation adaptable to this form of computing are extremely specific and limited. Moreover, living systems are not good at solving equations. It seems unlikely, therefore, that analog computers could contribute substantially in the quest to emulate living information processing systems, and in fact to date analog computers have made no such contribution.
As a class, traditional connectionist computing systems function as interpolation systems. The interpolation functions provided by many connectionist systems are derived from specific examples, called the "training set," that lists points in the domain space of the interpolation function and for each of those points, specifies the unique image point in the interpolation function's range space. Other connectionist systems utilize a training set consisting only of domain space points, developing their own unique and suitable range points for each of the domain points. The details of connectionist computing system architectures, designs, and operations vary widely, as do the means employed by each such systems to derive acceptable interpolation functions from training sets. For each training set, there are typically many alternative connectionist systems that will provide essentially equivalent results, but ultimately, all connectionist computing systems provide an appropriate mathematical mapping from one multidimensional space to another. Connectionist systems have proved to provide a very useful functionality, and one that may in fact be analogous to certain low level elements of animal intelligence systems, but it is hard to see how anything like the full range of animal intelligence information processing capabilities emerges solely from connectionist functionality.
Traditionally, the computing systems grouped under the term of "artificial intelligence" have focused directly on trying to duplicate or approach the various information processing characteristics of animal intelligence systems. Aspects of the field of artificial intelligence include expert systems, pattern recognition, robotics, heuristics, and a variety of data structuring, sorting, and searching techniques. There have been so many spectacular failures in the storied history of this field that today most computer scientists view it with some skepticism. Although isolated successes have been achieved, the often heard comment remains true that "these systems work for the cases to which they apply, and fail otherwise." At the heart of the failures lies the fact that artificial intelligence technology, as traditionally practiced, is very brittle. What is lacking is precisely the sort of robust, analogic capabilities always found in abundance in living information processing systems. This seems odd, given the extensive efforts expended over the years to achieve artificially intelligent systems, but it brings into sharp focus the inescapable fact that the entire computing industry has somehow "missed the point" in this matter!
It is not known today exactly how data is represented, stored, and manipulated in living information processing systems. The work to discover these secrets has been extensive and sustained over many years, and has been focused intensely on the biochemical, neurophysiological, and structural details of living neural systems. Some limited computational mechanisms have been identified by this work, but no mathematical data token for generically representing information in living neural systems has been described, no generic theory of data manipulation and computation in living neural systems has been set forth, and most importantly, no comprehensive, general theory has emerged that describes and accounts for the computational capabilities of animal intelligence systems in ways that are sufficient to support the engineering of new, non-living systems capable of exhibiting similar information processing characteristics.